library(igraph)
## 
## Attaching package: 'igraph'
## The following objects are masked from 'package:stats':
## 
##     decompose, spectrum
## The following object is masked from 'package:base':
## 
##     union
library(tidyverse)
## ── Attaching packages
## ───────────────────────────────────────
## tidyverse 1.3.2 ──
## ✔ ggplot2 3.4.0     ✔ purrr   1.0.2
## ✔ tibble  3.2.1     ✔ dplyr   1.1.2
## ✔ tidyr   1.3.1     ✔ stringr 1.5.0
## ✔ readr   2.1.3     ✔ forcats 0.5.2
## Warning: package 'tidyr' was built under R version 4.2.3
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::as_data_frame() masks tibble::as_data_frame(), igraph::as_data_frame()
## ✖ purrr::compose()       masks igraph::compose()
## ✖ tidyr::crossing()      masks igraph::crossing()
## ✖ dplyr::filter()        masks stats::filter()
## ✖ dplyr::lag()           masks stats::lag()
## ✖ purrr::simplify()      masks igraph::simplify()
library(questionr)

Read data file

#read file in as members
mt_edgelist <- read.csv("../data cleaning/all-data.csv")

View(mt_edgelist)
head(mt_edgelist)

Note: the sender is in the first column and receiver in the second

people <- filter(mt_edgelist, sourcetweet_type == "quoted" | is.na(sourcetweet_type))
print(unique(people$user_username))
##  [1] "mathyadriana"    "mathprofcarrie"  "pwr2dppl"        "xl772"          
##  [5] "WanderingPoint"  "LoboWithACause"  "qsideinstitute"  "frontLahn"      
##  [9] "RachelleDeCoste" "monsoon0"        "elizabethmunch"  "MarissaKawehi"  
## [13] "fra_berni"       "HthrLynnJ"       "sbagley"         "thewordninja_bk"
## [17] "matthematician"  "DrMRLD"          "VictorPiercey1"  "kmkinnaird"     
## [21] "jensilvermath"   "KJMDPhD"         "MattBoelkins"    "susan_dagostino"
## [25] "FedericoArdila"  "MBarany"         "DrBKirkpatrick"  "CKalinecCraig"  
## [29] "BikeMath"        "evainfeld"       "belladoesmath"   "BenisConsumer"  
## [33] "BresockCalculus" "katebelin"       "joshlaison"      "GailHWebster"   
## [37] "JohannaF_math"   "ClaudioJacobo"   "virtualcourtney" "StatsSam"       
## [41] "EWhitteck"       "siwelwerd"       "Shelby_M_Scott"  "blkmathmagic"   
## [45] "darrenglass"     "SoledadVillar5"  "diazlopezmath"   "aksir"          
## [49] "benjamindickman" "DrHenryGarrett"  "mathcourtney"    "kneppkatt"      
## [53] "swirlgirl31415"  "DrBrandyMath"    "AliciaPL25"      "GoodsiePhD"     
## [57] "DamienSBS"       "dtkung"          "jacksonfulton2"  "saracharala"    
## [61] "DrClaireJanelle" "sleepymathgrl"   "SuzanneSindi"    "sister_stem"    
## [65] "holom0rph"       "mathillustrated" "achambertloir"   "blviray"        
## [69] "BillOpenmind"    "Cayleygrapher"   "tritesprite"     "extremefriday"  
## [73] "RG1gal"          "KristinePelatt"  "maryepilgrim"    "malagonmath"    
## [77] "ef_math"         "matthewando"
#Careful, this overcounts!  The 'text' field is truncated for retweets, need to load
#new data file that includes sourcetweet_text field
JointMath<-mt_edgelist[mt_edgelist$user_username == 'JointMath',]
#did not use any of the hashtags or did not include a @JointMath tag
NotHT20<-subset(JointMath, !grepl("#JMM2020", JointMath$sourcetweet_text ), )
NotHT<-subset(NotHT20, !grepl("#JMM2021", NotHT20$sourcetweet_text ), )
Notpinged<-subset(NotHT, !grepl("@JointMath", NotHT$sourcetweet_text ))

# for example - this has nothing that says RT me...
Notpinged$text[12]
## [1] "RT @blkmathmagic: The SECOND #DisruptJMM slide from @mathprofcarrie\n: Math is not neutral\n\nThe more there are, the more comfortable I feel…"
Notpinged

Clearly the JointMath handle was RTing JMM tweets

What are our top group tweeting about?

influencer_names = c("WanderingPoint","mathprofcarrie","sbagley","pwr2dppl","RG1gal","LoboWithACause","VictorPiercey1")
#influencers <- filter(mt_edgelist, user_username %in% influencer_names)

codes <- colnames(mt_edgelist)[54:88]

for (person in influencer_names) {
  influencers <- filter(mt_edgelist, user_username == person)
  #Drop retweets
  influencersOTQT <- filter(influencers, sourcetweet_type == "quoted" | is.na(sourcetweet_type))
  
  n=nrow(influencersOTQT)
  influencer_tweets <- c()
  cnames <- c()
  for (code in codes) {
    freq=length(which(influencersOTQT[code]=="True"))/n
    if (freq>0){ 
      influencer_tweets <- append(influencer_tweets,freq)
      cnames <- append(cnames,code)}
  }

names(influencer_tweets) <- cnames
influencer_tweets <- sort(influencer_tweets,decreasing=TRUE)
print(person)
print(n)
print(knitr::kable(influencer_tweets))
cat("\n\n")
}
## [1] "WanderingPoint"
## [1] 10
## 
## 
## |                   |   x|
## |:------------------|---:|
## |amplification      | 0.6|
## |talk.support       | 0.5|
## |hashtags           | 0.4|
## |representation     | 0.3|
## |indigenous         | 0.3|
## |influencers        | 0.2|
## |emotional.support  | 0.2|
## |BC.impact          | 0.2|
## |SO.impact          | 0.2|
## |conversation       | 0.2|
## |jmm.experience     | 0.2|
## |gender             | 0.1|
## |advocacy           | 0.1|
## |CC.impact          | 0.1|
## |math.practice      | 0.1|
## |strategic.planning | 0.1|
## |humanize           | 0.1|
## |allyship.in.math   | 0.1|
## 
## 
## [1] "mathprofcarrie"
## [1] 47
## 
## 
## |                      |         x|
## |:---------------------|---------:|
## |talk.support          | 0.3829787|
## |conversation          | 0.3617021|
## |amplification         | 0.3404255|
## |hashtags              | 0.1914894|
## |strategic.planning    | 0.1914894|
## |influencers           | 0.1702128|
## |BC.impact             | 0.1702128|
## |representation        | 0.1489362|
## |bipoc                 | 0.1063830|
## |power                 | 0.1063830|
## |thankful              | 0.0851064|
## |belonging             | 0.0851064|
## |indigenous            | 0.0638298|
## |gender                | 0.0425532|
## |emotional.support     | 0.0425532|
## |math.practice         | 0.0425532|
## |lgbtq                 | 0.0212766|
## |SO.impact             | 0.0212766|
## |learning              | 0.0212766|
## |virtual.participation | 0.0212766|
## |humanize              | 0.0212766|
## |allyship.in.math      | 0.0212766|
## |math.experience       | 0.0212766|
## |futures               | 0.0212766|
## |activation            | 0.0212766|
## 
## 
## [1] "sbagley"
## [1] 42
## 
## 
## |                 |         x|
## |:----------------|---------:|
## |hashtags         | 0.6666667|
## |influencers      | 0.6428571|
## |amplification    | 0.5714286|
## |talk.support     | 0.5238095|
## |humanize         | 0.2619048|
## |power            | 0.2380952|
## |math.practice    | 0.2142857|
## |lgbtq            | 0.1904762|
## |advocacy         | 0.1904762|
## |bipoc            | 0.1190476|
## |representation   | 0.0952381|
## |BC.impact        | 0.0952381|
## |learning         | 0.0476190|
## |economic         | 0.0476190|
## |gender           | 0.0238095|
## |belonging        | 0.0238095|
## |conversation     | 0.0238095|
## |allyship.in.math | 0.0238095|
## 
## 
## [1] "pwr2dppl"
## [1] 34
## 
## 
## |                   |         x|
## |:------------------|---------:|
## |hashtags           | 0.5000000|
## |amplification      | 0.2941176|
## |talk.support       | 0.2647059|
## |BC.impact          | 0.2647059|
## |strategic.planning | 0.2352941|
## |influencers        | 0.2058824|
## |representation     | 0.2058824|
## |power              | 0.2058824|
## |indigenous         | 0.1470588|
## |conversation       | 0.1470588|
## |bipoc              | 0.1176471|
## |emotional.support  | 0.1176471|
## |current.events     | 0.1176471|
## |gender             | 0.0588235|
## |thankful           | 0.0588235|
## |SO.impact          | 0.0588235|
## |belonging          | 0.0588235|
## |humanize           | 0.0588235|
## |jmm.experience     | 0.0588235|
## |advocacy           | 0.0294118|
## |learning           | 0.0294118|
## 
## 
## [1] "RG1gal"
## [1] 2
## 
## 
## |              |   x|
## |:-------------|---:|
## |talk.support  | 0.5|
## |influencers   | 0.5|
## |math.practice | 0.5|
## |conversation  | 0.5|
## |humanize      | 0.5|
## |futures       | 0.5|
## 
## 
## [1] "LoboWithACause"
## [1] 16
## 
## 
## |                      |      x|
## |:---------------------|------:|
## |amplification         | 0.5625|
## |hashtags              | 0.3125|
## |representation        | 0.2500|
## |BC.impact             | 0.2500|
## |strategic.planning    | 0.2500|
## |thankful              | 0.1875|
## |talk.support          | 0.1250|
## |advocacy              | 0.1250|
## |belonging             | 0.1250|
## |power                 | 0.1250|
## |jmm.experience        | 0.1250|
## |bipoc                 | 0.0625|
## |gender                | 0.0625|
## |influencers           | 0.0625|
## |emotional.support     | 0.0625|
## |SO.impact             | 0.0625|
## |virtual.participation | 0.0625|
## |math.practice         | 0.0625|
## |humanize              | 0.0625|
## |math.experience       | 0.0625|
## 
## 
## [1] "VictorPiercey1"
## [1] 14
## 
## 
## |                   |         x|
## |:------------------|---------:|
## |talk.support       | 0.5714286|
## |amplification      | 0.5714286|
## |hashtags           | 0.4285714|
## |influencers        | 0.2857143|
## |math.practice      | 0.2857143|
## |belonging          | 0.1428571|
## |power              | 0.1428571|
## |strategic.planning | 0.1428571|
## |humanize           | 0.1428571|
## |gender             | 0.0714286|
## |representation     | 0.0714286|
## |BC.impact          | 0.0714286|
## |SO.impact          | 0.0714286|
## |indigenous         | 0.0714286|
## |SJEDI.impact       | 0.0714286|
## |allyship.in.math   | 0.0714286|
## |disrupteverything  | 0.0714286|

Distribution of SJEDI and CC Math codes for top tweeters

top_people = data.frame( name= c("WanderingPoint","mathprofcarrie","sbagley","pwr2dppl","LoboWithACause","VictorPiercey1"))
#influencers <- filter(mt_edgelist, user_username %in% influencer_names)
print(top_people)
##             name
## 1 WanderingPoint
## 2 mathprofcarrie
## 3        sbagley
## 4       pwr2dppl
## 5 LoboWithACause
## 6 VictorPiercey1
codes <- c("representation","lbgtq","bipoc","gender","indigenous","power","advocacy","learning","economic","SJEDI.impact","humanize","jmm.experience","math.experience","futures","math.practice","current.events","allyship.in.math","CC.impact")

for (code in codes) {
  column = c()
  for (person in top_people$name){
   influencers <- filter(filter(mt_edgelist, user_username == person), sourcetweet_type == "quoted" | is.na(sourcetweet_type))
  #Drop retweets
#   influencersOTQT <- filter(influencers, sourcetweet_type == "quoted" | is.na(sourcetweet_type))
  
  n=nrow(influencers)
  freq=length(which(influencers[[code]]=="True"))/n
  column <- append(column,freq)
  }

  top_people[[code]] <- column
}
 
#num=c() 
#for (person in top_people$name){
#   num <- append(num, nrow(filter(filter(mt_edgelist, user_username == person), sourcetweet_type == "quoted" | is.na(sourcetweet_type))))
#}
#top_people$n <- num

#print(top_people)


top_people %>% 
  pivot_longer(cols = codes, names_to="code") %>%
  ggplot(aes(code, value, fill = name)) +
  geom_col(position = position_dodge(), color = "black") +
  #scale_alpha_manual(values = c(0.5, 1), guide = guide_none()) +
  facet_grid(~code, scales = "free_x", switch = "x") +
  theme(strip.placement  = "outside",
        panel.spacing    = unit(0, "points"),
        strip.background = element_blank(),
        axis.text.x=element_blank(), 
        strip.text.x = element_text(angle = 90)
      #axis.ticks.x=element_blank()
        ) +
        #strip.text       = element_text(face = "bold", size = 12)) +
  labs(x = "Codes")
## Warning: Using an external vector in selections was deprecated in tidyselect 1.1.0.
## ℹ Please use `all_of()` or `any_of()` instead.
##   # Was:
##   data %>% select(codes)
## 
##   # Now:
##   data %>% select(all_of(codes))
## 
## See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

What are our netweavers tweeting about?

influencer_names = c("mathprofcarrie","JointMath","pwr2dppl","LoboWithACause","sbagley")
#influencers <- filter(mt_edgelist, user_username %in% influencer_names)

codes <- colnames(mt_edgelist)[54:88]

for (person in influencer_names) {
  influencers <- filter(mt_edgelist, user_username == person)
  #Drop retweets
  #influencersOTQT <- filter(influencers, sourcetweet_type == "quoted" | is.na(sourcetweet_type))
  
  n=nrow(influencers)
  influencer_tweets <- c()
  cnames <- c()
  for (code in codes) {
    freq=length(which(influencers[code]=="True"))/n
    if (freq>0){ 
      influencer_tweets <- append(influencer_tweets,freq)
      cnames <- append(cnames,code)}
  }

names(influencer_tweets) <- cnames
influencer_tweets <- sort(influencer_tweets,decreasing=TRUE)
print(person)
print(n)
print(knitr::kable(influencer_tweets))
cat("\n\n")
}
## [1] "mathprofcarrie"
## [1] 126
## 
## 
## |                      |         x|
## |:---------------------|---------:|
## |amplification         | 0.5476190|
## |talk.support          | 0.4682540|
## |hashtags              | 0.3730159|
## |influencers           | 0.2619048|
## |representation        | 0.1984127|
## |conversation          | 0.1587302|
## |strategic.planning    | 0.1349206|
## |power                 | 0.1269841|
## |BC.impact             | 0.1190476|
## |indigenous            | 0.1111111|
## |thankful              | 0.0952381|
## |humanize              | 0.0952381|
## |bipoc                 | 0.0873016|
## |gender                | 0.0873016|
## |math.practice         | 0.0714286|
## |advocacy              | 0.0555556|
## |learning              | 0.0476190|
## |allyship.in.math      | 0.0396825|
## |lgbtq                 | 0.0317460|
## |belonging             | 0.0317460|
## |current.events        | 0.0317460|
## |emotional.support     | 0.0238095|
## |SO.impact             | 0.0238095|
## |CC.impact             | 0.0158730|
## |math.experience       | 0.0158730|
## |jmm.experience        | 0.0158730|
## |disrupteverything     | 0.0158730|
## |virtual.participation | 0.0079365|
## |futures               | 0.0079365|
## |economic              | 0.0079365|
## |activation            | 0.0079365|
## 
## 
## [1] "JointMath"
## [1] 85
## 
## 
## |                      |         x|
## |:---------------------|---------:|
## |talk.support          | 0.6588235|
## |amplification         | 0.5411765|
## |influencers           | 0.4588235|
## |hashtags              | 0.4588235|
## |representation        | 0.1882353|
## |math.practice         | 0.1411765|
## |humanize              | 0.1294118|
## |thankful              | 0.1176471|
## |BC.impact             | 0.1058824|
## |gender                | 0.0941176|
## |indigenous            | 0.0941176|
## |power                 | 0.0823529|
## |strategic.planning    | 0.0823529|
## |advocacy              | 0.0705882|
## |bipoc                 | 0.0470588|
## |lgbtq                 | 0.0470588|
## |conversation          | 0.0470588|
## |allyship.in.math      | 0.0470588|
## |belonging             | 0.0352941|
## |learning              | 0.0352941|
## |SO.impact             | 0.0235294|
## |CC.impact             | 0.0235294|
## |jmm.experience        | 0.0235294|
## |emotional.support     | 0.0117647|
## |SJEDI.impact          | 0.0117647|
## |virtual.participation | 0.0117647|
## |math.experience       | 0.0117647|
## |futures               | 0.0117647|
## |activation            | 0.0117647|
## |current.events        | 0.0117647|
## 
## 
## [1] "pwr2dppl"
## [1] 101
## 
## 
## |                   |         x|
## |:------------------|---------:|
## |amplification      | 0.5049505|
## |talk.support       | 0.4158416|
## |hashtags           | 0.3861386|
## |influencers        | 0.2772277|
## |representation     | 0.1980198|
## |BC.impact          | 0.1485149|
## |power              | 0.1485149|
## |strategic.planning | 0.1485149|
## |bipoc              | 0.1188119|
## |indigenous         | 0.1188119|
## |humanize           | 0.1188119|
## |math.practice      | 0.1089109|
## |thankful           | 0.0891089|
## |gender             | 0.0792079|
## |advocacy           | 0.0792079|
## |lgbtq              | 0.0693069|
## |emotional.support  | 0.0693069|
## |SO.impact          | 0.0594059|
## |conversation       | 0.0594059|
## |jmm.experience     | 0.0594059|
## |current.events     | 0.0594059|
## |belonging          | 0.0495050|
## |CC.impact          | 0.0198020|
## |math.experience    | 0.0198020|
## |SJEDI.impact       | 0.0099010|
## |learning           | 0.0099010|
## |allyship.in.math   | 0.0099010|
## |activation         | 0.0099010|
## 
## 
## [1] "LoboWithACause"
## [1] 48
## 
## 
## |                      |         x|
## |:---------------------|---------:|
## |amplification         | 0.6250000|
## |talk.support          | 0.4791667|
## |hashtags              | 0.3333333|
## |representation        | 0.2083333|
## |influencers           | 0.1666667|
## |power                 | 0.1666667|
## |strategic.planning    | 0.1666667|
## |indigenous            | 0.1458333|
## |humanize              | 0.1458333|
## |gender                | 0.1250000|
## |thankful              | 0.1250000|
## |BC.impact             | 0.1041667|
## |advocacy              | 0.1041667|
## |belonging             | 0.1041667|
## |math.practice         | 0.1041667|
## |bipoc                 | 0.0833333|
## |lgbtq                 | 0.0625000|
## |allyship.in.math      | 0.0625000|
## |emotional.support     | 0.0416667|
## |math.experience       | 0.0416667|
## |jmm.experience        | 0.0416667|
## |SO.impact             | 0.0208333|
## |virtual.participation | 0.0208333|
## |conversation          | 0.0208333|
## |futures               | 0.0208333|
## |current.events        | 0.0208333|
## 
## 
## [1] "sbagley"
## [1] 60
## 
## 
## |                   |         x|
## |:------------------|---------:|
## |amplification      | 0.6166667|
## |talk.support       | 0.5500000|
## |hashtags           | 0.5333333|
## |influencers        | 0.5166667|
## |humanize           | 0.2166667|
## |power              | 0.2000000|
## |lgbtq              | 0.1666667|
## |math.practice      | 0.1666667|
## |advocacy           | 0.1500000|
## |bipoc              | 0.1000000|
## |representation     | 0.1000000|
## |gender             | 0.0666667|
## |BC.impact          | 0.0666667|
## |allyship.in.math   | 0.0666667|
## |thankful           | 0.0500000|
## |current.events     | 0.0500000|
## |indigenous         | 0.0333333|
## |belonging          | 0.0333333|
## |learning           | 0.0333333|
## |strategic.planning | 0.0333333|
## |economic           | 0.0333333|
## |emotional.support  | 0.0166667|
## |conversation       | 0.0166667|
## |math.experience    | 0.0166667|
#What was @JointMath tweeting about?
jointmath <- filter(mt_edgelist, user_username == "JointMath")
n=nrow(jointmath)
codes <- colnames(jointmath)[54:88]
joint_tweets <- c()
for (code in codes) {
  joint_tweets <- append(joint_tweets, length(which(jointmath[code]=="True"))/n)
}
names(joint_tweets) <- codes
joint_tweets <- sort(joint_tweets,decreasing=TRUE)
print(joint_tweets)
##          talk.support         amplification           influencers 
##            0.65882353            0.54117647            0.45882353 
##              hashtags        representation         math.practice 
##            0.45882353            0.18823529            0.14117647 
##              humanize              thankful             BC.impact 
##            0.12941176            0.11764706            0.10588235 
##                gender            indigenous                 power 
##            0.09411765            0.09411765            0.08235294 
##    strategic.planning              advocacy                 bipoc 
##            0.08235294            0.07058824            0.04705882 
##                 lgbtq          conversation      allyship.in.math 
##            0.04705882            0.04705882            0.04705882 
##             belonging              learning             SO.impact 
##            0.03529412            0.03529412            0.02352941 
##             CC.impact        jmm.experience     emotional.support 
##            0.02352941            0.02352941            0.01176471 
##          SJEDI.impact virtual.participation       math.experience 
##            0.01176471            0.01176471            0.01176471 
##               futures            activation        current.events 
##            0.01176471            0.01176471            0.01176471 
##     disrupteverything              economic           celebration 
##            0.00000000            0.00000000            0.00000000 
##                 troll                beyond 
##            0.00000000            0.00000000
print(n)
## [1] 85

Who is tweeting about important things?

#important_codes <- c("representation","gender","indigenous","advocacy","power","bipoc")
#
#for (code in important_codes){
#  code_only <- filter(mt_edgelist, (!!as.symbol(code))=="True")
#  print(code)
#  top_tweeters=sort(table(code_only$user_username),decreasing=TRUE)
  #print(head(top_tweeters,10))
#  barplot(head(top_tweeters,10),las=2)
#}

Who is tweeting about SJEDI and CC in Math?

themes=c("SJEDI","Creating.change.in.math", "Building.community", "Broadening.the.counterpublic", "Self.organization")
for (theme in themes){
print(theme)
code_only <- filter(filter(mt_edgelist, (!!as.symbol(theme))==1), sourcetweet_type == "quoted" | is.na(sourcetweet_type))
top_tweeters=sort(wtd.table(x=code_only$user_username, weights=1+code_only$retweet_count+code_only$quote_count),decreasing=TRUE)
barplot(head(top_tweeters,10),las=2)
}
## [1] "SJEDI"

## [1] "Creating.change.in.math"

## [1] "Building.community"

## [1] "Broadening.the.counterpublic"

## [1] "Self.organization"

# Who is tweeting about SJEDI and CC in Math? unweighted version

themes=c("SJEDI","Creating.change.in.math", "Building.community", "Broadening.the.counterpublic", "Self.organization")
for (theme in themes){
print(theme)
code_only <- filter(filter(mt_edgelist, (!!as.symbol(theme))==1), sourcetweet_type == "quoted" | is.na(sourcetweet_type))
#top_tweeters=sort(wtd.table(x=code_only$user_username, weights=1+code_only$retweet_count+code_only$quote_count),decreasing=TRUE)
 top_tweeters=sort(table(code_only$user_username),decreasing=TRUE)
barplot(head(top_tweeters,10),las=2)
}
## [1] "SJEDI"

## [1] "Creating.change.in.math"

## [1] "Building.community"

## [1] "Broadening.the.counterpublic"

## [1] "Self.organization"

Can we do code level too?

codes <- colnames(mt_edgelist)[54:88]
for (code in codes){
print(code)
code_only <- filter(filter(mt_edgelist, (!!as.symbol(code))=="True"), sourcetweet_type == "quoted" | is.na(sourcetweet_type))

#Weighted
#top_tweeters=sort(wtd.table(x=code_only$user_username, weights=1+code_only$retweet_count+code_only$quote_count),decreasing=TRUE)

#Unweighted
top_tweeters=sort(table(code_only$user_username),decreasing=TRUE)

barplot(head(top_tweeters,5),las=2)
}
## [1] "bipoc"

## [1] "gender"

## [1] "lgbtq"

## [1] "talk.support"

## [1] "amplification"

## [1] "influencers"

## [1] "representation"

## [1] "thankful"

## [1] "emotional.support"

## [1] "BC.impact"

## [1] "SO.impact"

## [1] "hashtags"

## [1] "indigenous"

## [1] "advocacy"

## [1] "CC.impact"

## [1] "SJEDI.impact"

## [1] "belonging"

## [1] "learning"

## [1] "virtual.participation"

## [1] "power"

## [1] "math.practice"

## [1] "strategic.planning"

## [1] "conversation"

## [1] "humanize"

## [1] "allyship.in.math"

## [1] "math.experience"

## [1] "jmm.experience"

## [1] "futures"

## [1] "disrupteverything"

## [1] "economic"

## [1] "activation"

## [1] "current.events"

## [1] "celebration"

## [1] "troll"

## [1] "beyond"